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基于数据驱动和实时监控的洪水场景的现时预测技术的研究
Yue Zheng1, Xiaoming Jing2, Yonggang Lin3
1The Institute of Municipal Engineering, Zhejiang University, Hangzhou, China.
概括
本研究介绍了一种使用机器学习和实时数据的快速城市洪水预测方法. 它准确预测洪水深度,帮助快速城市化地区的应急管理.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 全球气候变化和城市化加快了城市洪水风险,特别是在发展中国家.
- 有效的城市洪水应急管理依赖于实时监测和预测洪水范围和排水系统.
研究的目的:
- 通过实时监测,提出一个快速的,数据驱动的城市洪水预测方法.
- 为紧急洪水风险管理提供技术支持.
主要方法:
- 利用具有有限监控点的机器学习算法推断全球实时城市水位.
- 开发了一种基于数据的方法,用于动态的城市洪水预测,整合实时监测和降水预测.
主要成果:
- 水平扣除方法的平均平均绝对误差 (MAE) 为0.101,城市洪水的根平均平方误差 (RMSE) 为0.144,管道系统的0.124和0.162.
- 概率统计分析表明,与管道系统相比,洪水深度扣除更为稳定.
- 城市洪水现在预测方法在测试中准确预测了R平方值为0.973和0.962的洪水深度.
结论:
- 开发的方法为实时城市洪水预测提供了强大的解决方案.
- 准确预测洪水深度和范围,有助于加强紧急洪水风险管理策略.
- 该方法为减轻城市洪水影响提供了至关重要的技术支持.
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